As an AI Architect, you will bring deep expertise in Artificial Intelligence, Machine Learning, Generative AI, Coding Assistants, AI Assistants, and Agile software engineering practices. You will guide the full AI lifecycle from conceptualization and solution design to model development, testing, and successful deployment into production environments. The ideal candidate will have a proven architectural background, hands‑on experience with modern AI platforms, and a strong track record of delivering complex, enterprise‑grade intelligent systems. You will collaborate closely with cross-functional technology and business teams to shape the future of our AI ecosystem and accelerate innovation across the organization.
Must Have Skills:
- Strong hands-on programming expertise in at least one modern language such as Python, C#, Java, or JavaScript, with the ability to guide engineering teams on best practices.
- Deep proficiency with AI/ML frameworks and libraries including TensorFlow, PyTorch, and Keras, with the ability to architect scalable training and inference pipelines.
- Advanced understanding of Generative AI architectures, including GANs, VAEs, transformer-based models, and experience applying these techniques to real-world enterprise use cases.
- Practical experience using AI Coding Assistants
- Hands-on expertise with Agentic AI platforms, including Microsoft Copilot Agents, AutoGPT, CrewAI, Anthropic Claude, and other autonomous workflow systems.
- Strong command of Cloud & MLOps platforms, such as AWS SageMaker, Azure Machine Learning, Google AI Platform, including experience building automated model deployment, monitoring, and retraining pipelines.
- Solid experience architecting solutions on major cloud platforms (AWS, Azure, GCP), including familiarity with cloud-native AI, data, and security services.
- Proven ability to mentor, guide, and provide architectural direction to junior and mid-level AI/ML engineers across multiple projects.
- Ability to conduct AI/ML and Generative AI code reviews, ensuring adherence to architectural standards, performance benchmarks, and responsible AI practices.
- Strong analytical and problem‑solving skills, including the ability to rapidly diagnose and resolve complex issues in AI systems, ML pipelines, and application runtimes.
- Excellent communication skills, capable of translating complex AI concepts for both technical teams and business stakeholders.
- Experience defining enterprise AI architectures, reference models, reusable design patterns, and establishing organization-wide architectural standards.
- Ability to define and implement AI governance frameworks, including Responsible AI guidelines, compliance controls, model guardrails, safety protocols, and risk-mitigation practices.
Responsibilities:
AI & Machine Learning Architecture
- Architect, design, and guide the implementation of enterprise grade AI and ML solutions that solve complex business problems and power intelligent applications.
- Lead collaboration with data scientists, ML engineers, platform engineers, and software teams to integrate AI capabilities into products, platforms, and enterprise workflows.
- Provide hands-on technical leadership in Python, including proficiency with ML, GenAI, and LLM frameworks (TensorFlow, PyTorch, HuggingFace, LangChain, etc.).
- Oversee end-to-end data preparation, feature engineering, vectorization, and model development, ensuring data quality and readiness for LLMs and Generative AI workloads.
- Continuously evaluate emerging advancements in AI, ML, LLMs, Agentic Systems, and incorporate relevant innovations into architectural roadmaps and solution designs.
- Optimize AI and Generative AI models for performance, latency, scalability, observability, and cost efficiency, leveraging cloud-native and distributed compute environments.
- Conduct and drive technical design reviews, code reviews, and architecture assessments to ensure high standards of quality and engineering rigor.
- Diagnose, troubleshoot, and resolve complex AI system issues to maintain the reliability, accuracy, and security of production AI applications.
Generative AI, Copilots & Coding Assistants
- Design and architect enterprise-wide capabilities leveraging AI coding assistants, copilots, and developer productivity tools (e.g., GitHub Copilot, Azure AI Studio assistants).
- Lead the development of extensions, plugins, and integrations for IDEs like Visual Studio Code, IntelliJ IDEA, and Eclipse to embed AI-driven developer workflows.
- Apply deep understanding of prompt engineering, LLM behaviour, context-window optimization, fine‑tuning, and agentic workflows to enhance solution performance.
- Design, train, and evaluate custom generative models, exploring architectures such as GANs, VAEs, diffusion models, and transformer-based architectures.
- Architect pipelines for data acquisition, cleaning, text chunking, retrieval augmentation (RAG), and ingestion into vector stores.
- Integrate enterprise solutions with AI platforms and APIs such as OpenAI, Azure OpenAI, LangChain, and other orchestration frameworks.
- Design and maintain the LLM data layer using technologies like Azure Cosmos DB, Azure AI Search, vector stores, and embedding indexes.
- Develop agentic automation solutions using frameworks such as LangChain, LlamaIndex, Semantic Kernel, and AutoGen, and integrate them with enterprise systems.
- Apply strong DevOps practices using CI/CD pipelines, GitHub Actions, Azure DevOps, and modern testing frameworks to maintain robust and automated AI development workflows.
- Contribute to or lead copilot initiatives across domains such as application development, customer support, operations, and productivity automation.
Prompt Engineering
- Design, test, and refine high‑quality prompts, prompt templates, and generative AI interaction models tailored to organizational use cases.
- Work closely with product, content, and data teams to align prompt behaviour with business outcomes, brand voice, and user experience requirements.
- Continuously improve prompt structures to enhance accuracy, relevance, safety, and contextual understanding within AI/LLM systems.
- Experiment with novel prompting strategies - chain-of-thought, retrieval-augmented prompts, tool-augmented prompts, system message optimization - to improve AI performance.
- Build and maintain an enterprise prompt library, ensuring versioning, governance, reuse, and integration into automated workflows and applications.
- Participate in fine-tuning and reinforcement learning processes by designing ground‑truth prompts, evaluation prompts, and tuning datasets.
- Diagnose and refine prompts that yield suboptimal or incorrect results, ensuring predictable and aligned LLM behaviour across use cases.
